Nikan Noorafkan This workflow corresponds to n8n.io template #10062 — we link there as the canonical source.
This workflow follows the Agent → Google Sheets recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"nodes": [
{
"id": "9c5d8412-ea52-4af7-a129-f3808418de5c",
"name": "Monthly Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"notes": "Runs 1st of every month to analyze last 30 days",
"position": [
-140,
-60
],
"parameters": {
"rule": {
"interval": [
{
"field": "cronExpression",
"expression": "0 0 1 * *"
}
]
}
},
"typeVersion": 1
},
{
"id": "69c788b5-fdc2-4407-86c2-13dffe38bf61",
"name": "Get Performance Data",
"type": "n8n-nodes-base.httpRequest",
"notes": "Gets 30-day performance data via GAQL. Min 100 impressions for statistical validity.",
"position": [
140,
-60
],
"parameters": {
"url": "=https://googleads.googleapis.com/{{$env.GOOGLE_ADS_API_VERSION}}/customers/{{$env.GOOGLE_ADS_CUSTOMER_ID}}/googleAds:search",
"method": "POST",
"options": {},
"jsonBody": "={\"query\": \"SELECT ad_group_ad.ad.id, ad_group_ad.ad.responsive_search_ad.headlines, ad_group.name, metrics.impressions, metrics.clicks, metrics.ctr FROM ad_group_ad WHERE segments.date DURING LAST_30_DAYS AND metrics.impressions > 100 ORDER BY metrics.clicks DESC LIMIT 1000\"}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "googleAdsOAuth2Api"
},
"credentials": {
"googleAdsOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "fb6f2b02-9ca1-4260-a761-d1fa23b6e926",
"name": "Prepare Performance Data",
"type": "n8n-nodes-base.code",
"notes": "Analyzes performance data, groups by category/theme, calculates CTRs, formats for AI",
"position": [
520,
-60
],
"parameters": {
"jsCode": "// Format performance data for AI analysis\n\nconst response = $input.first().json;\nconst results = response.results || [];\n\n// Group by category (ad group) and extract headlines\nconst categoryData = {};\nconst themeData = {};\n\nresults.forEach(result => {\n const category = result.adGroup?.name || 'Unknown';\n const headlines = result.adGroupAd?.ad?.responsiveSearchAd?.headlines || [];\n const headline = headlines[0]?.text || '';\n const ctr = parseFloat(result.metrics?.ctr || 0);\n const impressions = parseInt(result.metrics?.impressions || 0);\n const clicks = parseInt(result.metrics?.clicks || 0);\n \n if (impressions < 100) return;\n \n // Aggregate by category\n if (!categoryData[category]) {\n categoryData[category] = {impressions: 0, clicks: 0, ads: []};\n }\n categoryData[category].impressions += impressions;\n categoryData[category].clicks += clicks;\n categoryData[category].ads.push({headline, ctr, impressions, clicks});\n \n // Track themes\n const themes = ['vegan', 'organic', 'natural', 'premium', 'sale', 'new', 'free shipping'];\n themes.forEach(theme => {\n if (headline.toLowerCase().includes(theme)) {\n if (!themeData[theme]) {\n themeData[theme] = {impressions: 0, clicks: 0, count: 0};\n }\n themeData[theme].impressions += impressions;\n themeData[theme].clicks += clicks;\n themeData[theme].count += 1;\n }\n });\n});\n\n// Calculate CTRs\nconst categoryAnalysis = Object.entries(categoryData).map(([cat, data]) => ({\n category: cat,\n ctr: ((data.clicks / data.impressions) * 100).toFixed(2),\n total_impressions: data.impressions,\n total_clicks: data.clicks,\n ad_count: data.ads.length\n})).sort((a, b) => parseFloat(b.ctr) - parseFloat(a.ctr));\n\nconst themeAnalysis = Object.entries(themeData).map(([theme, data]) => ({\n theme,\n ctr: ((data.clicks / data.impressions) * 100).toFixed(2),\n total_impressions: data.impressions,\n ad_count: data.count\n})).sort((a, b) => parseFloat(b.ctr) - parseFloat(a.ctr));\n\nreturn {\n total_ads_analyzed: results.length,\n date_range: 'Last 30 days',\n top_categories: categoryAnalysis.slice(0, 5),\n bottom_categories: categoryAnalysis.slice(-5),\n top_themes: themeAnalysis.slice(0, 5),\n bottom_themes: themeAnalysis.slice(-5),\n analysis_prompt: `Analyze this Google Ads performance data and provide actionable insights:\\n\\nTop Performing Categories:\\n${JSON.stringify(categoryAnalysis.slice(0, 5), null, 2)}\\n\\nBottom Performing Categories:\\n${JSON.stringify(categoryAnalysis.slice(-5), null, 2)}\\n\\nTop Performing Themes:\\n${JSON.stringify(themeAnalysis.slice(0, 5), null, 2)}\\n\\nBottom Performing Themes:\\n${JSON.stringify(themeAnalysis.slice(-5), null, 2)}\\n\\nProvide 3-5 specific recommendations for improving ad copy. Include estimated impact percentages. Format as: {'recommendations': [{'action': '...', 'expected_impact': '...', 'priority': 'HIGH/MEDIUM/LOW'}]}`\n};"
},
"typeVersion": 2
},
{
"id": "15384935-f99f-4e06-a475-b232106fe256",
"name": "AI Agent - Analyze Performance",
"type": "@n8n/n8n-nodes-langchain.agent",
"notes": "n8n AI Agent analyzes patterns and provides insights",
"position": [
800,
-60
],
"parameters": {
"text": "={{$json.analysis_prompt}}",
"options": {
"systemMessage": "You are a Google Ads performance analyst. Analyze the data and provide specific, actionable recommendations. Return valid JSON only."
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "9adc1284-fa91-43c3-b326-260a51cfb000",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"notes": "Lower temperature (0.2) for analytical tasks",
"position": [
780,
120
],
"parameters": {
"model": "gpt-4o",
"options": {
"maxTokens": 2000,
"temperature": 0.2
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "e5f12de9-b23c-4052-872e-466a9148b404",
"name": "Generate Report",
"type": "n8n-nodes-base.code",
"notes": "Creates formatted report with AI insights",
"position": [
1260,
-60
],
"parameters": {
"jsCode": "// Parse AI recommendations and create report\n\nconst aiOutput = $input.first().json.output;\nconst performanceData = $node['Prepare Performance Data'].json;\n\n// Parse JSON from AI\nlet recommendations;\ntry {\n const jsonMatch = aiOutput.match(/```json\\s*([\\s\\S]*?)```/) || aiOutput.match(/{[\\s\\S]*}/);\n recommendations = JSON.parse(jsonMatch ? jsonMatch[1] || jsonMatch[0] : aiOutput);\n} catch (e) {\n recommendations = {recommendations: [{action: aiOutput, priority: 'HIGH'}]};\n}\n\nconst report = `# 30-Day Performance Analysis Report\n\n## Executive Summary\nAnalyzed: ${performanceData.total_ads_analyzed} ads\nPeriod: ${performanceData.date_range}\nDate: ${new Date().toISOString().split('T')[0]}\n\n## Top Performing Categories\n${performanceData.top_categories.map(c => `- ${c.category}: ${c.ctr}% CTR (${c.ad_count} ads)`).join('\\n')}\n\n## Top Performing Themes\n${performanceData.top_themes.map(t => `- \"${t.theme}\" messaging: ${t.ctr}% CTR (${t.ad_count} ads)`).join('\\n')}\n\n## AI-Powered Recommendations\n${recommendations.recommendations?.map((r, i) => `${i+1}. [${r.priority}] ${r.action}\\n Expected Impact: ${r.expected_impact || 'TBD'}`).join('\\n\\n')}\n\n## Next Steps\n1. Update ad copy to emphasize top-performing themes\n2. A/B test recommendations\n3. Review again in 30 days\n\n---\nGenerated by n8n AI Agent + OpenAI\n`;\n\nreturn {\n report_markdown: report,\n recommendations: recommendations.recommendations || [],\n top_themes: performanceData.top_themes,\n generated_at: new Date().toISOString()\n};"
},
"typeVersion": 2
},
{
"id": "ef39ae9e-d5f1-4ac5-83d2-648d70637564",
"name": "Save Report to Sheets",
"type": "n8n-nodes-base.googleSheets",
"notes": "Archives report in Google Sheets",
"position": [
1500,
-60
],
"parameters": {
"options": {},
"sheetName": {
"__rl": true,
"mode": "name",
"value": "Performance Reports"
},
"documentId": {
"__rl": true,
"mode": "id",
"value": "={{$env.GOOGLE_SHEET_ID}}"
}
},
"typeVersion": 4.4
},
{
"id": "875c5cb7-e6d2-4576-89ad-334e88668200",
"name": "Send Report",
"type": "n8n-nodes-base.slack",
"notes": "Sends report to team via Slack",
"position": [
1720,
-60
],
"parameters": {
"text": "={{$json.report_markdown}}",
"otherOptions": {}
},
"typeVersion": 2.1
},
{
"id": "77996afd-b9cb-49d1-a0b4-996210484fa6",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-260,
-360
],
"parameters": {
"width": 280,
"height": 220,
"content": "## \ud83d\udfe8 1 \u201cMonthly Trigger\u201d\n\n\ud83d\udd53 Runs every month\nAutomatically starts on the 1st of each month to analyze the last 30 days of Google Ads performance."
},
"typeVersion": 1
},
{
"id": "7175f013-792a-4e70-80d3-035ec5cd485c",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-40,
140
],
"parameters": {
"width": 360,
"height": 220,
"content": "## \ud83d\udfe8 2 \u201cGet Performance Data\u201d\n\n\ud83d\udcca Fetches ad metrics\nQueries Google Ads API for CTR, clicks, and impressions using GAQL.\nFilters out ads with fewer than 100 impressions for accurate analysis."
},
"typeVersion": 1
},
{
"id": "439ff524-d0ad-4a84-b697-be637f70447f",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
380,
-400
],
"parameters": {
"width": 420,
"height": 200,
"content": "## \ud83d\udfe8 3 \u201cPrepare Performance Data\u201d\n\n\ud83e\uddee Processes raw metrics\nGroups ads by category and common themes (e.g., \u201csale,\u201d \u201cfree shipping\u201d).\nCalculates average CTRs and builds a summary prompt for AI analysis."
},
"typeVersion": 1
},
{
"id": "16c3d3b7-7f19-4c31-8b72-161f8d7c5b8c",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
740,
260
],
"parameters": {
"width": 360,
"height": 220,
"content": "## \ud83d\udfe8 4 \u201cAI Agent - Analyze Performance\u201d\n\n\ud83e\udd16 AI insight generation\nSends the summary data to GPT-4o for analysis.\nThe AI recommends optimizations and highlights top-performing messaging."
},
"typeVersion": 1
},
{
"id": "fb0e5d69-95b4-4cd4-834a-b5f563fb367a",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
1080,
-400
],
"parameters": {
"width": 420,
"height": 200,
"content": "## \ud83d\udfe8 5 \u201cGenerate Report\u201d\n\n\ud83e\uddfe Creates readable summary\nConverts AI output into a formatted markdown report.\nIncludes actionable recommendations and key performance stats."
},
"typeVersion": 1
}
],
"connections": {
"Generate Report": {
"main": [
[
{
"node": "Save Report to Sheets",
"type": "main",
"index": 0
}
]
]
},
"Monthly Trigger": {
"main": [
[
{
"node": "Get Performance Data",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent - Analyze Performance",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Get Performance Data": {
"main": [
[
{
"node": "Prepare Performance Data",
"type": "main",
"index": 0
}
]
]
},
"Save Report to Sheets": {
"main": [
[
{
"node": "Send Report",
"type": "main",
"index": 0
}
]
]
},
"Prepare Performance Data": {
"main": [
[
{
"node": "AI Agent - Analyze Performance",
"type": "main",
"index": 0
}
]
]
},
"AI Agent - Analyze Performance": {
"main": [
[
{
"node": "Generate Report",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
googleAdsOAuth2ApiopenAiApi
Pro
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About this workflow
This workflow automatically analyzes Google Ads performance every month, using the Google Ads API and OpenAI (GPT-4o) to uncover which ad themes, categories, and messages perform best. It then generates a structured AI report, saves it to Google Sheets, and sends a Slack summary…
Source: https://n8n.io/workflows/10062/ — original creator credit. Request a take-down →
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